Facebook may have given us a peek at the future of Timeline after a new design rolled out for one of ABC News’ producers Wednesday. Apparently Facebook is experimenting with new ways of laying out user’s pages. Facebook confirmed today that it’s trying some new things. “This is a new design Facebook is testing with a small percentage of people to make navigating timeline even easier,” a Facebook spokesperson told ABC News.

Yesterday, egregious financial truth-tellers (a client of ours at easyDNS) ZeroHedge broke the news that parties unknown, engineered what looks to be a textbook "pump-and-dump" on Twitter's stock by putting up a fake "Bloomberg Financial News" site...

Deep neural networks are an approach to machine learning that has revolutionized computer vision and speech recognition in the last few years, blowing the previous state of the art results out of the water. They’ve also brought promising results to many other areas, including language understanding and machine translation. Despite this, it remains challenging to understand what, exactly, these networks are doing.

Understanding neural networks is just scratching the surface, however, because understanding the network is fundamentally tied to understanding the data it operates on. The combination of neural networks and dimensionality reduction turns out to be a very interesting tool for visualizing high-dimensional data – a much more powerful tool than dimensionality reduction on its own.

Paragraph vectors, introduced by Le & Mikolov (2014), are vectors that represent chunks of text. Paragraph vectors come in a few variations but the simplest one, which we are using here, is basically some really nice features on top of a bag of words representation.

With word embeddings, we learn vectors in order to solve a language task involving the word. With paragraph vectors, we learn vectors in order to predict which words are in a paragraph.

Concretely, the neural network learns a low-dimensional approximation of word statistics for different paragraphs. In the hidden representation of this neural network, we get vectors representing each paragraph. These vectors have nice properties, in particular that similar paragraphs are close together.

Now, Google has some pretty awesome people. Andrew Dai, Quoc Le, and Greg Corrado decided to create paragraph vectors for some very interesting data sets. One of those was Wikipedia, creating a vector for every English Wikipedia article. The result is that we get a visualization of the entirety of Wikipedia. A map of Wikipedia. A large fraction of Wikipedia’s articles fall into a few broad topics: sports, music (songs and albums), films, species, and science.

"High-Tech Bridge offers a groundbreaking innovation with their ImmuniWeb on-demand web security testing service" says Steve Morgan, founder and CEO at Cybersecurity Ventures, and Editor-In-Chief of the Cybersecurity Market Report and Cybersecurity 500 list of the world's hottest and most innovative cybersecurity companies. "ImmuniWeb is so simple that a complete technical novice can order and schedule a website security assessment in less than five minutes. There is a huge cybersecurity labor shortage and companies are struggling with complex security solutions. ImmuniWeb makes protecting and defending websites easy, the way it should be" adds Morgan.

WikiLeaks is finally back in the business of accepting truly anonymous leaks four and a half years after its last submission system went down amid infighting between its leaders and disenchanted staffers.

Worried that you might have been targeted with Hacking Team spyware, but don't know how to find out for sure? IT security firm Rook Security has released Milano, a free automated tool meant to detect the Hacking Team malware on a computer system.

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